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      "execution_count": 5,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Requirement already satisfied: catboost in /usr/local/lib/python3.10/dist-packages (1.2)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (from catboost) (0.20.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from catboost) (3.7.1)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.23.5)\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.5.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from catboost) (1.10.1)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from catboost) (5.13.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from catboost) (1.16.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2022.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.1.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (0.11.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (4.42.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.4.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (23.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (3.1.1)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->catboost) (8.2.2)\n"
          ]
        }
      ]
    },
    {
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "import catboost\n",
        "from catboost import CatBoostClassifier\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "Z1qMj4CHHaw6"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
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      "source": [
        "df=pd.read_csv(\"titanic3.csv\")"
      ],
      "metadata": {
        "id": "HEPac3t0Hd9u"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
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      "source": [
        "df.shape"
      ],
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        },
        "id": "UIm2OJKaHfk_",
        "outputId": "a4e05625-3eaa-4861-9987-5769c75e8889"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1309, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 8
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    {
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      "source": [
        "df.head()"
      ],
      "metadata": {
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          "height": 319
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        "id": "Ps1Z8nAAHlfT",
        "outputId": "cf9aa7c0-6c46-4b0c-d824-c6b480c14ed2"
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      "execution_count": 9,
      "outputs": [
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              "   pclass  survived                                             name     sex  \\\n",
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              "1       1         1                   Allison, Master. Hudson Trevor    male   \n",
              "2       1         0                     Allison, Miss. Helen Loraine  female   \n",
              "3       1         0             Allison, Mr. Hudson Joshua Creighton    male   \n",
              "4       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
              "\n",
              "     age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
              "0  29.00      0      0   24160  211.3375       B5        S    2    NaN   \n",
              "1   0.92      1      2  113781  151.5500  C22 C26        S   11    NaN   \n",
              "2   2.00      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
              "3  30.00      1      2  113781  151.5500  C22 C26        S  NaN  135.0   \n",
              "4  25.00      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
              "\n",
              "                         home.dest  \n",
              "0                     St Louis, MO  \n",
              "1  Montreal, PQ / Chesterville, ON  \n",
              "2  Montreal, PQ / Chesterville, ON  \n",
              "3  Montreal, PQ / Chesterville, ON  \n",
              "4  Montreal, PQ / Chesterville, ON  "
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              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-d803c96b-9e49-40dc-9aac-1b99e362079d button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6f7a9ed9-1f3c-442d-903a-9ae1940aadca button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6f7a9ed9-1f3c-442d-903a-9ae1940aadca');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vuvZydLIIBZU",
        "outputId": "5b22e6a2-5eb7-4338-e900-9a399d3e5278"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pclass         int64\n",
              "survived       int64\n",
              "name          object\n",
              "sex           object\n",
              "age          float64\n",
              "sibsp          int64\n",
              "parch          int64\n",
              "ticket        object\n",
              "fare         float64\n",
              "cabin         object\n",
              "embarked      object\n",
              "boat          object\n",
              "body         float64\n",
              "home.dest     object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zje7pKD1HncZ",
        "outputId": "04f78078-88fd-40c7-acd6-9559a455fe09"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pclass          0\n",
              "survived        0\n",
              "name            0\n",
              "sex             0\n",
              "age           263\n",
              "sibsp           0\n",
              "parch           0\n",
              "ticket          0\n",
              "fare            1\n",
              "cabin        1014\n",
              "embarked        2\n",
              "boat          823\n",
              "body         1188\n",
              "home.dest     564\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['embarked'] = df['embarked'].replace(np.NaN, 'Unknown')"
      ],
      "metadata": {
        "id": "0HJfypr8Ho07"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['cabin'] = df['cabin'].replace(np.NaN, 'U')\n",
        "df['cabin'] = [ln[0] for ln in df['cabin'].values]\n",
        "df['cabin'] = df['cabin'].replace('U', 'Unknown')"
      ],
      "metadata": {
        "id": "QBEKEYVBHrs7"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"cabin\"].isna()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YibKfwQLHuO-",
        "outputId": "95dda248-3305-4adb-bd72-6f69d6dec947"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       False\n",
              "1       False\n",
              "2       False\n",
              "3       False\n",
              "4       False\n",
              "        ...  \n",
              "1304    False\n",
              "1305    False\n",
              "1306    False\n",
              "1307    False\n",
              "1308    False\n",
              "Name: cabin, Length: 1309, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['cabin'].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8YRvK639Hvux",
        "outputId": "30a4d084-fef1-47e8-a5f1-aa740e3f6da1"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    B\n",
              "1    C\n",
              "2    C\n",
              "3    C\n",
              "4    C\n",
              "Name: cabin, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[[f for f in list(df) if f not in ['name', 'boat','body', 'ticket', 'home.dest']]]"
      ],
      "metadata": {
        "id": "MIl0oe2ZHxsT"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lblenc=LabelEncoder()\n",
        "\n",
        "df[\"sex\"]=lblenc.fit_transform(df[\"sex\"])\n",
        "\n",
        "df[\"pclass\"]=lblenc.fit_transform(df[\"pclass\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wPcRVNKpH0Q8",
        "outputId": "0e80dad6-ae00-4dbe-deab-10025d99dde2"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-17-42efb274acd4>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[\"sex\"]=lblenc.fit_transform(df[\"sex\"])\n",
            "<ipython-input-17-42efb274acd4>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[\"pclass\"]=lblenc.fit_transform(df[\"pclass\"])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['age']=df['age'].fillna(df['age'].mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tJcbn1MvH3P5",
        "outputId": "85bfc2e9-fd2b-4903-8fec-8fe1c27466ed"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-18-2f8578dfdb1e>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['age']=df['age'].fillna(df['age'].mean())\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "22etJ5HJH5Di",
        "outputId": "697b3feb-dcfa-48d8-80f6-c8628171305f"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pclass      0\n",
              "survived    0\n",
              "sex         0\n",
              "age         0\n",
              "sibsp       0\n",
              "parch       0\n",
              "fare        1\n",
              "cabin       0\n",
              "embarked    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.dropna()"
      ],
      "metadata": {
        "id": "EcXsnOFTH6MJ"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CD-4buiAH77e",
        "outputId": "9f52cd84-bc56-4643-cf1f-2b27e6459e1f"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1308, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=df.drop(labels=\"survived\",axis=1)\n",
        "y=df['survived']"
      ],
      "metadata": {
        "id": "o367U671H9PV"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features = [feat for feat in list(df) if feat != 'survived']\n",
        "titanic_categories = np.where(df[features].dtypes != np.float)[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pecc68UeIG1r",
        "outputId": "cd7d3d92-8807-40ca-8602-32cc8f1dac2d"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-23-bee2fd65c8cd>:2: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  titanic_categories = np.where(df[features].dtypes != np.float)[0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(df[features],\n",
        "                                                    df[['survived']],\n",
        "                                                    test_size=0.2,\n",
        "                                                     random_state=0)"
      ],
      "metadata": {
        "id": "k2E8aC6dIJwD"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "params = {'iterations':5000,\n",
        "        'learning_rate':0.01,\n",
        "        'cat_features':titanic_categories,\n",
        "        'depth':3,\n",
        "        'eval_metric':'AUC',\n",
        "        'verbose':200,\n",
        "        'od_type':\"Iter\",\n",
        "        'od_wait':500,\n",
        "        'random_seed': 0         }"
      ],
      "metadata": {
        "id": "THVxVwZ1ILXp"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_model = CatBoostClassifier(**params)\n",
        "cat_model.fit(X_train, y_train,\n",
        "          eval_set=(X_test, y_test),\n",
        "          use_best_model=True,\n",
        "          plot=True\n",
        "         );"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260,
          "referenced_widgets": [
            "fb017b501b294ce68e561234c97e1a71"
          ]
        },
        "id": "3JUNy_59IPAc",
        "outputId": "4c6dca2d-f41b-4afc-d46f-87bcdf26b255"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fb017b501b294ce68e561234c97e1a71"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0:\ttest: 0.8412152\tbest: 0.8412152 (0)\ttotal: 13.4ms\tremaining: 1m 6s\n",
            "200:\ttest: 0.8476416\tbest: 0.8483181 (182)\ttotal: 529ms\tremaining: 12.6s\n",
            "400:\ttest: 0.8516696\tbest: 0.8516696 (399)\ttotal: 1.2s\tremaining: 13.8s\n",
            "600:\ttest: 0.8574811\tbest: 0.8574811 (600)\ttotal: 1.89s\tremaining: 13.8s\n",
            "800:\ttest: 0.8592952\tbest: 0.8602792 (760)\ttotal: 2.48s\tremaining: 13s\n",
            "1000:\ttest: 0.8597872\tbest: 0.8603407 (968)\ttotal: 3.2s\tremaining: 12.8s\n",
            "1200:\ttest: 0.8589263\tbest: 0.8603407 (968)\ttotal: 4.71s\tremaining: 14.9s\n",
            "1400:\ttest: 0.8578808\tbest: 0.8603407 (968)\ttotal: 5.78s\tremaining: 14.9s\n",
            "Stopped by overfitting detector  (500 iterations wait)\n",
            "\n",
            "bestTest = 0.8603406925\n",
            "bestIteration = 968\n",
            "\n",
            "Shrink model to first 969 iterations.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "zn3n63CNB-fr",
        "outputId": "0d4faa31-3a6d-4c24-a48b-185332980744"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "y_pred = cat_model.predict(X_test)\n",
        "cm = confusion_matrix(y_test, [1 if p > 0.5 else 0 for p in y_pred])\n",
        "plt.ticklabel_format( axis='y', useOffset=False)\n",
        "sns.heatmap(cm, annot=True, annot_kws={\"size\": 16})"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_test, [1 if p > 0.5 else 0 for p in y_pred])\n",
        "\n",
        "print(\"Accuracy:\", accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DHiIvi5qITvz",
        "outputId": "e97132be-25b8-4e3c-c53d-108205fa7014"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8282442748091603\n"
          ]
        }
      ]
    }
  ]
}